Existing causal corpora were not built to support our goal for open-ended causal generation given any free-form textual input: as in neural machine translation (NMT), we need a large training set with millions of examples. Thus we harvest a large causal dataset from the preprocessed large-scale English Common Crawl corpus (5.14 TB) [1]}. The key guidelines of our dataset are as follows: 1) The causal relation is explicitly expressed in text with a causal pattern e.g. `because'; 2) The `cause' and `effect' arguments must both appear in the same sentence; 3) The `cause' and `effect' arguments can be of any length of contiguous text without overlaps between them; 4) Negative causal relations are filtered.
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